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The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.

The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.

The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.

The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

Location-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.

The industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.

The coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.

This study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.

An industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

This paper aims to present the classification, representation and extraction of adhesively bonded assembly features (ABAFs) from the computer-aided design (CAD) model.

The ABAFs are represented as a set of faces with a characteristic arrangement among the faces among parts in proximity suitable for adhesive bonding. The characteristics combination of the faying surfaces and their topological relationships help in classification of ABAFs. The ABAFs are classified into elementary and compound types based on the number of assembly features exist at the joint location.

A set of algorithms is developed to extract and identify the ABAFs from CAD model. Typical automotive and aerospace CAD assembly models have been used to illustrate and validate the proposed approach.

New classification and extraction methods for ABAFs are proposed, which are useful for variant design.

The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array.

A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification.

E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection.

The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

Forensic dentistry is the application of dentistry in legal proceedings that arise from any facts relating to teeth. The ultimate goal of forensic odontology is to identify the individual when there are no other means of identification such as fingerprint, Deoxyribonucleic acid (DNA), iris, hand print and leg print. The purpose of selecting dental record is for the teeth to be able to withstand decomposition, heat degradation up to 1600 ^°C. Dental patterns are unique for every individual. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic dental images for person identification.

To achieve an accurate identification of individuals, the missing tooth in the radiograph has to be identified before matching of ante-mortem (AM) and post-mortem (PM) radiographs. To identify whether the missing tooth is a molar or premolar, each tooth in the given radiograph has to be classified using a k-nearest neighbor (k-NN) classifier; then, it is matched with the universal tooth numbering system. In order to make exact person identification, this research work is mainly concentrate on contour shape extraction and texture feature extraction for person identification. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic images for individual identification. Then, shape matching of AM and PM images is performed by similarity and distance metric for accurate person identification.

The experimental results are analyzed for shape and feature extraction of both radiographic and photographic dental images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model, and it is well suited for forensic odontologists to identify a person in mass disaster situations.

Forensic odontology is a branch of human identification that uses dental evidence to identify the victims. In mass disaster circumstances, contours and dental patterns are very useful to extract the shape in individual identification.

The experimental results are analyzed both the contour shape extraction and texture feature extraction of both radiographic and photographic images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model and it is well suited for forensic odontologists to identify a person in mass disaster situations. The findings provide theoretical and practical implications for individual identification of both radiographic and photographic images with a view to accurate identification of the person.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

Displacement measurement in large-scale structures (such as excavation walls) is one of the most important applications of close-range photogrammetry, in which achieving high precision requires extracting and accurately matching local features from convergent images. The purpose of this study is to introduce a new multi-image pointing (MIP) algorithm is introduced based on the characteristics of the geometric model generated from the initial matching. This self-adaptive algorithm is used to correct and improve the accuracy of the extracted positions from local features in the convergent images.

In this paper, the new MIP algorithm based on the geometric characteristics of the model generated from the initial matching was introduced, which in a self-adaptive way corrected the extracted image coordinates. The unique characteristics of this proposed algorithm were that the position correction was accomplished with the help of continuous interaction between the 3D model coordinates and the image coordinates and that it had the least dependency on the geometric and radiometric nature of the images. After the initial feature extraction and implementation of the MIP algorithm, the image coordinates were ready for use in the displacement measurement process. The combined photogrammetry displacement adjustment (CPDA) algorithm was used for displacement measurement between two epochs. Micro-geodesy, target-based photogrammetry and the proposed MIP methods were used in a displacement measurement project for an excavation wall in the Velenjak area in Tehran, Iran, to evaluate the proposed algorithm performance. According to the results, the measurement accuracy of the point geo-coordinates of 8 mm and the displacement accuracy of 13 mm could be achieved using the MIP algorithm. In addition to the micro-geodesy method, the accuracy of the results was matched by the cracks created behind the project’s wall. Given the maximum allowable displacement limit of 4 cm in this project, the use of the MIP algorithm produced the required accuracy to determine the critical displacement in the project.

Evaluation of the results demonstrated that the accuracy of 8 mm in determining the position of the points on the feature and the accuracy of 13 mm in the displacement measurement of the excavation walls could be achieved using precise positioning of local features on images using the MIP algorithm.The proposed algorithm can be used in all applications that need to achieve high accuracy in determining the 3D coordinates of local features in close-range photogrammetry.

Some advantages of the proposed MIP photogrammetry algorithm, including the ease of obtaining observations and using local features on the structure in the images rather than installing the artificial targets, make it possible to effectively replace micro-geodesy and instrumentation methods. In addition, the proposed MIP method is superior to the target-based photogrammetric method because it does not need artificial target installation and protection. Moreover, in each photogrammetric application that needs to determine the exact point coordinates on the feature, the proposed algorithm can be very effective in providing the possibility to achieve the required accuracy according to the desired objectives.

This paper aims to improve a sentiment analysis (SA) system to help users (i.e. customers or hotel managers) understand hotel evaluations. There are three main purposes in this paper: designing an unsupervised method for extracting online Chinese features and opinion pairs, distinguishing different intensities of polarity in opinion words and examining the changes in polarity in the time series.

In this paper, a review analysis system is proposed to automatically capture feature opinions experienced by other tourists presented in the review documents. In the system, a feature-level SA is designed to determine the polarity of these features. Moreover, an unsupervised method using a part-of-speech pattern clarification query and multi-lexicons SA to summarize all Chinese reviews is adopted.

The authors expect this method to help travellers search for what they want and make decisions more efficiently. The experimental results show the F-measure of the proposed method to be 0.628. It thus outperforms the methods used in previous studies.

The study is useful for travellers who want to quickly retrieve and summarize helpful information from the pool of messy hotel reviews. Meanwhile, the system will assist hotel managers to comprehensively understand service qualities with which guests are satisfied or dissatisfied.